In this section, we discuss the ideas underlying the design of TV-stable learning algorithms. We first give the definition of differential privacy (DP), which will be a key tool.
Differential privacy is a notion of data privacy, introduced in [1]}, defined as follows.
[Differential privacy (DP)]

An algorithm \(\mathcal {A}\)  satisfies \((\epsilon ,\delta )\) -differential privacy if for any two neighboring datasets \(S\)  and \(S^{\prime }\) , differing in one sample, for any measurable event \(\in \text{Range}()\) ,
\(\mathbb {P}(\mathcal {A}(S)\in ) \le e^\epsilon \mathbb {P}(\mathcal {A}(S^{\prime })\in )+\delta \) 
